Pranay Sharma

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Pramod VarshneyDistinguished Professor of EECS, Syracuse UniversityEmail yang diverifikasi di syr.edu
Gauri JoshiCarnegie Mellon UniversityEmail yang diverifikasi di andrew.cmu.edu
Prashant KhanduriWayne State University, MIEmail yang diverifikasi di wayne.edu
Donald J. Bucci Jr.Lockheed Martin Advanced Technology LabsEmail yang diverifikasi di ieee.org
Sijia LiuMichigan State University & MIT-IBM Watson AI LabEmail yang diverifikasi di msu.edu
Ketan RajawatIIT KanpurEmail yang diverifikasi di iitk.ac.in
Sai Kiran BulusuThe Ohio State UniversityEmail yang diverifikasi di osu.edu
Parikshit RamIBM ResearchEmail yang diverifikasi di ibm.com
Mingyi HongAssociate Professor, University of Minnesota; Amazon ScholarEmail yang diverifikasi di umn.edu
Augustin Alexandru SAUCANUniversity assistant at TU WienEmail yang diverifikasi di tuwien.ac.at
Yuguang YaoMichigan State UniversityEmail yang diverifikasi di msu.edu
Jiancheng LiuMichigan State UniversityEmail yang diverifikasi di mails.tsinghua.edu.cn
Siva Theja MaguluriGeorgia Institute of TechnologyEmail yang diverifikasi di gatech.edu
Sajad KhodadadianAssistant Professor, Virginia TechEmail yang diverifikasi di vt.edu
Jia (Kevin) LiuAssociate Professor of Electrical and Computer Engineering, The Ohio State UniversityEmail yang diverifikasi di ece.osu.edu
Haibo YangRochester Institute of TechnologyEmail yang diverifikasi di rit.edu
Rohan PandaMLE @ Inworld.ai | M.S. Computer Engineering, Carnegie Mellon University | B.E. Electrical andEmail yang diverifikasi di andrew.cmu.edu
Divyansh JhunjhunwalaCarnegie Mellon UniversityEmail yang diverifikasi di andrew.cmu.edu
Swatantra KafleSyracuse UniversityEmail yang diverifikasi di syr.edu
Zheng XuGoogle ResearchEmail yang diverifikasi di google.com

Ikuti

Pranay Sharma

C-MInDS, IIT Bombay

Email yang diverifikasi di andrew.cmu.edu - Beranda

Machine Learning Stochastic Optimization Nonconvex Optimization Min-max Optimization Reinforcement Learning


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Model sparsity can simplify machine unlearning J Jia, J Liu, P Ram, Y Yao, G Liu, Y Liu, P Sharma, S Liu Advances in Neural Information Processing Systems 36, 51584-51605, 2023	85	2023
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling S Khodadadian, P Sharma, G Joshi, ST Maguluri International Conference on Machine Learning, 10997-11057, 2022	78	2022
Stem: A stochastic two-sided momentum algorithm achieving near-optimal sample and communication complexities for federated learning P Khanduri, P Sharma, H Yang, M Hong, J Liu, K Rajawat, P Varshney Advances in Neural Information Processing Systems 34, 6050-6061, 2021	77	2021
Fedvarp: Tackling the variance due to partial client participation in federated learning D Jhunjhunwala, P Sharma, A Nagarkatti, G Joshi Uncertainty in Artificial Intelligence, 906-916, 2022	61	2022
Decentralized Gaussian filters for cooperative self-localization and multi-target tracking P Sharma, AA Saucan, DJ Bucci, PK Varshney IEEE Transactions on Signal Processing 67 (22), 5896-5911, 2019	58	2019
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms P Sharma, R Panda, G Joshi, PK Varshney International Conference on Machine Learning, 19683-19730, 2022	54	2022
Communication Network Topology Inference via Transfer Entropy P Sharma, DJ Bucci, SK Brahma, PK Varshney IEEE Transactions on Network Science and Engineering, 2019	43	2019
On the convergence of federated averaging with cyclic client participation YJ Cho, P Sharma, G Joshi, Z Xu, S Kale, T Zhang International Conference on Machine Learning, 5677-5721, 2023	31	2023
On distributed online convex optimization with sublinear dynamic regret and fit P Sharma, P Khanduri, L Shen, DJ Bucci, PK Varshney 2021 55th Asilomar conference on signals, systems, and computers, 1013-1017, 2021	28	2021
Model sparsification can simplify machine unlearning J Jia, J Liu, P Ram, Y Yao, G Liu, Y Liu, P Sharma, S Liu arXiv preprint arXiv:2304.04934 1 (2), 3, 2023	26	2023
Why interpretability in machine learning? an answer using distributed detection and data fusion theory KR Varshney, P Khanduri, P Sharma, S Zhang, PK Varshney arXiv preprint arXiv:1806.09710, 2018	26	2018
On noise-enhanced distributed inference in the presence of Byzantines M Gagrani, P Sharma, S Iyengar, VSS Nadendla, A Vempaty, H Chen, ... 2011 49th Annual Allerton Conference on Communication, Control, and …, 2011	21	2011
Parallel Restarted SPIDER--Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity P Sharma, S Kafle, P Khanduri, S Bulusu, K Rajawat, PK Varshney arXiv preprint arXiv:1912.06036, 2019	20	2019
Federated minimax optimization with client heterogeneity P Sharma, R Panda, G Joshi arXiv preprint arXiv:2302.04249, 2023	12	2023
Byzantine resilient non-convex svrg with distributed batch gradient computations P Khanduri, S Bulusu, P Sharma, PK Varshney arXiv preprint arXiv:1912.04531, 2019	12	2019
On distributed stochastic gradient descent for nonconvex functions in the presence of byzantines S Bulusu, P Khanduri, P Sharma, PK Varshney ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020	10	2020
What is missing in IRM training and evaluation? challenges and solutions Y Zhang, P Sharma, P Ram, M Hong, K Varshney, S Liu arXiv preprint arXiv:2303.02343, 2023	7	2023
Distributed stochastic non-convex optimization: Momentum-based variance reduction P Khanduri, P Sharma, S Kafle, S Bulusu, K Rajawat, PK Varshney arXiv preprint arXiv:2005.00224, 2020	7	2020
Byzantine resilient non-convex SCSG with distributed batch gradient computations S Bulusu, P Khanduri, S Kafle, P Sharma, PK Varshney IEEE Transactions on Signal and Information Processing over Networks 7, 754-766, 2021	4	2021
On Self-Localization and Tracking with an Unknown Number of Targets P Sharma, AA Saucan, DJ Bucci, PK Varshney 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 1735 - 1739, 2019	4	2019

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